Investigating the relative value of health and social care related quality of life using a discrete choice experiment

A key outcome in the evaluation of health technologies is the quality adjusted life year (QALY) which is often estimated using health measures such as the EuroQol instruments (EQ-5D-3L and EQ-5D-5L). The impacts of many interventions extend beyond a narrow definition of health to include non-health impacts such as social care related dimensions of quality of life (QoL). This means that there are circumstances where the QALY does not capture the full value of an intervention. In response to this, instruments with a wider measurement framework, such as the Adult Social Care Outcomes Toolkit (ASCOT), which measures social care related QoL, have been developed. Given the range of instruments available, it is important that decision-makers have tools to assess value for money comprehensively and consistently. To date, preference elicitation of different aspects of QoL combined within the same valuation procedure has not been tested. We investigate the relationship between health and social care aspects of QoL when assessed jointly by combining EQ-5D-5L and ASCOT in an online discrete choice experiment (DCE). In July 2016, 975 respondents recruited from internet panels completed 15 choice sets from an underlying design of 300. Conditional logit regression was used to estimate coefficient decrements for each attribute and examine their relative magnitude. Latent class and mixed logit modelling were used to understand preference heterogeneity. The results suggest trading across health and social care aspects indicated by coefficient estimates of differing magnitude. Dimensions with the largest disutility include four from EQ-5D-5L and one from ASCOT. There is evidence of preference heterogeneity at more severe dimension levels. We have used an established method to test the joint valuation of concepts measuring different aspects of QoL. The results have implications for the aspects of QoL that are included in QALY estimation and used in resource allocation decision-making

Anadromy and marine habitat use of Lake trout (Salvelinus namaycush) from the central Canadian Arctic

Anadromy was documented in 16 lake trout, Salvelinus namaycush, from Canada\u27s central Arctic using capture data and otolith microchemistry. For the first time, estuarine/marine habitat use was described for five individuals using acoustic telemetry. Age-at-first-migration to sea was variable (10–39 years) among individuals and most S. namaycush undertook multiple anadromous migrations within their lifetime. Telemetry data suggested that S. namaycush do not travel far into marine habitats and prefer surface waters (\u3c2 m). These results further our collective understanding of the marine ecology of Arctic S. namaycush

Annual survival probabilities of anadromous arctic char remain high and stable despite interannual differences in sea ice melt date

Throughout their range, anadromous Arctic Char (Salvelinus alpinus (Linnaeus, 1758)) support commercial, recreational, and subsistence fisheries that are important economically, socially, and culturally. However, drivers of interannual variation in survival in this species remain poorly understood. Here, we aimed to quantify the impact of environmental and biological parameters on the survival probability of anadromous Arctic Char near the community of Cambridge Bay, Nunavut, Canada. To do so, we tracked 183 Arctic Char tagged with acoustic transmitters and used capture–mark–recapture methods to estimate survival probabilities over six years. Annual survival probabilities for individuals was high, varying between 0.79 and 0.88, whereas recapture probabilities varied between 0.64 and 0.90. Interannual variation in survival probability was low and neither the environmental (air temperature and sea ice cover) nor biological (sex) variables influenced survival probability. These estimates suggest that annual survival probability is high for anadromous adult Arctic Char in the Cambridge Bay area, despite clear differences in the ice cover melt date among years. These results further our understanding of the demographic parameters of Arctic Char in the region, which will be important for future assessments of the sustainability of commercial fisheries as well as for predicting population responses to a rapidly changing Arctic

On the various ways that anadromous salmonids use lake habitats to complete their life history

Despite the preponderance of exorheic lakes in rivers home to anadromous salmonids, little research has focused on how salmon, trout, and char use lakes as part of their anadromous life histories. The literature on this subject has so far revealed that some parr move into lakes to feed and grow before smoltification but that smolts moving through lakes tend to have high mortality in disproportion to what is observed in other habitats they migrate in or through. Adults have been observed using lakes for behavioural thermoregulation prior to spawning, and kelts of iteroparous species often exploit lakes to overwinter before returning to sea to recondition. We summarized knowledge on lakes as salmonid habitat and identified knowledge gaps about the use of lakes by anadromous salmonids related to whether lakes are barriers that structure genetics of populations, whether mortality in lakes is compensatory or additive, and whether systems with lakes have higher rates of repeat spawning among iteroparous salmonids. Human activities that alter lakes require further study to understand how changes in temperature, oxygen, ice, or circulation affect navigation and fate.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

From top to bottom: Do Lake Trout diversify along a depth gradient in Great Bear Lake, NT, Canada?

<div><p>Depth is usually considered the main driver of Lake Trout intraspecific diversity across lakes in North America. Given that Great Bear Lake is one of the largest and deepest freshwater systems in North America, we predicted that Lake Trout intraspecific diversity to be organized along a depth axis within this system. Thus, we investigated whether a deep-water morph of Lake Trout co-existed with four shallow-water morphs previously described in Great Bear Lake. Morphology, neutral genetic variation, isotopic niches, and life-history traits of Lake Trout across depths (0–150 m) were compared among morphs. Due to the propensity of Lake Trout with high levels of morphological diversity to occupy multiple habitat niches, a novel multivariate grouping method using a suite of composite variables was applied in addition to two other commonly used grouping methods to classify individuals. Depth alone did not explain Lake Trout diversity in Great Bear Lake; a distinct fifth deep-water morph was not found. Rather, Lake Trout diversity followed an ecological continuum, with some evidence for adaptation to local conditions in deep-water habitat. Overall, trout caught from deep-water showed low levels of genetic and phenotypic differentiation from shallow-water trout, and displayed higher lipid content (C:N ratio) and occupied a higher trophic level that suggested an potential increase of piscivory (including cannibalism) than the previously described four morphs. Why phenotypic divergence between shallow- and deep-water Lake Trout was low is unknown, especially when the potential for phenotypic variation should be high in deep and large Great Bear Lake. Given that variation in complexity of freshwater environments has dramatic consequences for divergence, variation in the complexity in Great Bear Lake (i.e., shallow being more complex than deep), may explain the observed dichotomy in the expression of intraspecific phenotypic diversity between shallow- vs. deep-water habitats. The ambiguity surrounding mechanisms driving divergence of Lake Trout in Great Bear Lake should be seen as reflective of the highly variable nature of ecological opportunity and divergent natural selection itself.</p></div

Length at age of Lake Trout captured in three depth zones.

<p>Length at age of Lake Trout captured in three depth zones (0−20 m = ○ and dotted line; 21−50 m = gray ○ and dashed line; 51–150 m = ♦ and solid line) and classified into three morphs (Morph 1 = ○ and dotted line; Morph 2 = ● and dashed line; Morph 3 = gray ○ and solid line) and four composite groups (Comp 1 = * and dotted line; Comp 2 = —and dashed line, Comp 3 = <b>×</b> and solid line; and Comp 4 = gray ▲ and long-dashed line) in Great Bear Lake.</p

Trend between C:N ratio and δ¹³C (‰) in individual Lake Trout from three depth strata.

<p>Trend between C:N ratio and δ¹³C (‰) in individual Lake Trout from Great Bear Lake caught from three depth strata: open circle = 0–20 m, light grey square = 21–50 m, and black diamond = 51–150 m. A polynomial trend line was fitted for the overall data. C:N ratios are an indirect representation of lipid content (index of buoyancy).</p

Admixture coefficient plots of the Bayesian clustering analysis for Lake Trout using STRUCTURE.

<p>Admixture coefficient plots of the Bayesian clustering analysis for Lake Trout from Great Bear Lake using STRUCTURE. Population structure was examined by groups defined by depth zone (0-20m, 21–50 m, 51–150 m), morphological data (Morph1, Morph 2 and Morph 3), and the composite dataset (Comp 1, Comp 2, Comp 3, and Comp 4). Each individual is represented as a vertical line partitioned into colored segments representative of an individual’s fractional membership in any given cluster (K). The most likely number of genetic clusters based on the ΔK statistic of Evanno et al. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0193925#pone.0193925.ref063" target="_blank">63</a>] was three, six and four for depth, morphology, and composite grouping respectively. The most likely number of clusters based on the traditional statistic mean LnP(K) was K = 1 for each scenario.</p

Global phenotypic trait divergence (<i>Pst</i>).

<p>Global phenotypic trait divergence (<i>Pst</i>) ± SE for individual variable for Lake Trout from Great Bear Lake based on groups established based on depth strata, morphological data, and composite data.</p